Tuple routing in a streaming application

ABSTRACT

A system and method for modifying the processing within a streaming application are disclosed. The method may determine one or more parameters for a tuple at a first stream operator. The one or more parameters may represent a processing history of the tuple at the first stream operator. The method may associate the one or more parameters with the tuple metadata. A second stream operator may modify the processing of the tuple if the parameter falls outside a threshold.

FIELD

This disclosure generally relates to stream computing, and inparticular, to computing applications that receive streaming data andprocess the data as it is received.

BACKGROUND

Database systems are typically configured to separate the process ofstoring data from accessing, manipulating, or using data stored in adatabase. More specifically, database systems use a model in which datais first stored and indexed in a memory before subsequent querying andanalysis. In general, database systems may not be well suited forperforming real-time processing and analyzing streaming data. Inparticular, database systems may be unable to store, index, and analyzelarge amounts of streaming data efficiently or in real time.

SUMMARY

Embodiments of the disclosure provide a method, system, and computerprogram product for processing data. The method, system, and computerprogram receive streaming data to be processed by a plurality ofprocessing elements comprising one or more stream operators.

Embodiments of the disclosure provide a method, system, and computerprogram product for processing data. The method, system, and computerprogram receive streaming data to be processed by a plurality ofprocessing elements comprising one or more stream operators.

One embodiment is directed to a method for processing a stream of tuplesin a streaming application. The method may include modifying processingof a tuple in a streaming application. The method may determine one ormore parameters for a tuple at a first stream operator. The one or moreparameters may represent a processing history of the tuple at the firststream operator. The method may associate the one or more parameterswith the tuple. A second stream operator may modify the processing ofthe tuple if the parameter falls outside a threshold.

Another embodiment is directed to a system for processing a stream oftuples in a streaming application. The system may determine whether tomodify the processing of a tuple in a streaming application. The systemmay include first and second stream operators. The first stream operatormay determine one or more parameters for a tuple, the one or moreparameters representing a processing history of the tuple at the firststream operator. The system may associate the one or more parameterswith the tuple. The second stream operator may modify the processing ofthe tuple if the parameter falls outside a threshold.

Yet another embodiment is directed to a computer program product.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a computing infrastructure configured to execute astream computing application according to various embodiments.

FIG. 2 illustrates a more detailed view of a compute node of FIG. 1according to various embodiments.

FIG. 3 illustrates a more detailed view of the management system of FIG.1 according to various embodiments.

FIG. 4 illustrates a more detailed view of the compiler system of FIG. 1according to various embodiments.

FIG. 5 illustrates a more detailed view of an operator graph in astream-based application according to various embodiments.

FIG. 6 illustrates a method to determine one or more data processingparameters in a streaming application, according to some embodiments.

FIG. 7 illustrates examples of data processing parameters correspondingto the method of FIG. 6 to add or modify the one or more data processingparameters to a tuple, according to some embodiments.

FIG. 8 illustrates a method to route tuples in a streaming application,according to some embodiments.

FIG. 9 illustrates a more detailed view of an operator graph for astreaming application in which processing of tuples may be modifiedbased on one or more data processing parameters, according to someembodiments.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in fractions of a second. Constructing anapplication using this type of processing has opened up a newprogramming paradigm that will allow for development of a broad varietyof innovative applications, systems, and processes, as well as presentnew challenges for application programmers and database developers.

In a stream-based computing application, stream operators are connectedto one another such that data flows from one stream operator to the next(e.g., over a TCP/IP socket). Stream operators may be classified intolevels. A level, as referred to herein, may be defined as a number ofsubsequent stream operators from a particular stream operator.Scalability is achieved by distributing an application across nodes bycreating executables (i.e., processing elements), as well as replicatingprocessing elements on multiple nodes and load balancing among them.Stream operators in a stream computing application can be fused togetherto form a processing element that is executable. Doing so allowsprocessing elements to share a common process space, resulting in muchfaster communication between stream operators than is available usinginter-process communication techniques (e.g., using a TCP/IP socket).Further, processing elements can be inserted or removed dynamically froman operator graph representing the flow of data through the streamcomputing application.

A “tuple” is data. More specifically, a tuple is a sequence of one ormore attributes associated with an entity. Examples of attributes may beany of a variety of different types, e.g., integer, float, Boolean,string, etc. The attributes may be ordered. A tuple may be extended byadding one or more additional attributes to it. In addition toattributes associated with an entity, a tuple may include metadata,i.e., data about the tuple. Metadata corresponding to a tuple mayinclude one or more data processing parameters. A data processingparameter, as used herein, may refer to various parameters that maydescribe the processing history of a tuple as it is processed in astreaming application. For example, a data processing parameter maycontain information describing the amount of time that a stream operatortakes to process a tuple. As used herein, “stream” or “data stream”refers to a sequence of tuples. Generally, a stream may be considered apseudo-infinite sequence of tuples.

A window, as referred to herein, is a logical container for tuplesreceived by an input port of a stream operator. Windowing may allow forcreation of subsets of data within a streaming application. A streamoperator may not necessarily support windowing by default. A streamoperator may, however, be configured to support windowing. Both tumblingand sliding windows may store tuples according to various conditions. Atumbling window may store incoming tuples until the window is full, thenmay trigger a stream operator behavior, flush all stored tuples from thewindow, and then may begin this process again. Conversely, a slidingwindow does not automatically flush the window when the triggercondition is fulfilled. A sliding window also has an eviction policythat tells the window when to flush the window and begin this processagain. These conditions may be referred to herein as windowingconditions. Windowing may be defined in any number of ways. For example,an application programmer may define one or more specific windowingconditions. Additionally, the system may provide a set of windowingconditions.

A punctuation is a control signal that appears interleaved with thetuples in a stream. The punctuation may, for example, notify the streamoperator of the grouping of tuples to be processed. An example of wherepunctuation may be used is within an Aggregate stream operator. Everytime the stream operator receives a punctuation, it may aggregate theaccumulated tuples since the last window punctuation.

Stream computing applications handle massive volumes of data that needto be processed efficiently and in real time. For example, a streamcomputing application may continuously ingest and analyze hundreds ofthousands of messages per second and up to petabytes of data per day.Accordingly, each stream operator in a stream computing application maybe required to process a received tuple within fractions of a second.

Embodiments disclosed herein are directed to methods and apparatusesthat enhance the ability of a streaming application to efficiently andrapidly process a received data stream. In one embodiment, a streamoperator may be configured to determine information describing how aparticular tuple has been processed throughout the streamingapplication. The processing information for a particular tuple mayinclude details about how the tuple has been processed in the streamingapplication and may include processing time information, informationabout changes to the data, or information about events the data may havecaused. This processing information may be used to set one or more dataprocessing parameters. Data processing parameters may also be referredto as parameters. The value of the data processing parameters may becompared with a corresponding threshold value that may allow thestreaming application to determine how to process the tuple. If thevalue of the data processing parameter falls outside the threshold, thetuple may require additional processing or modified processing, and maybe routed down an execution path in accordance with that determination.The processing determination may be made without considering the valueof the tuple attributes.

FIG. 1 illustrates one exemplary computing infrastructure 100 that maybe configured to execute a stream-based computing application, accordingto some embodiments. The computing infrastructure 100 includes amanagement system 105 and two or more compute nodes 110A-110D—i.e.,hosts—which are communicatively coupled to each other using one or morecommunications networks 120. The communications network 120 may includeone or more servers, networks, or databases, and may use a particularcommunication protocol to transfer data between the compute nodes110A-110D. A compiler system 102 may be communicatively coupled with themanagement system 105 and the compute nodes 110 either directly or viathe communications network 120.

FIG. 2 is a more detailed view of a compute node 110, which may be thesame as one of the compute nodes 110A-110D of FIG. 1, according tovarious embodiments. The compute node 110 may include, withoutlimitation, one or more processors (CPUs) 205, a network interface 215,an interconnect 220, a memory 225, and a storage 230. The compute node110 may also include an I/O device interface 210 used to connect I/Odevices 212, e.g., keyboard, display, and mouse devices, to the computenode 110.

Each CPU 205 retrieves and executes programming instructions stored inthe memory 225 or storage 230. Similarly, the CPU 205 stores andretrieves application data residing in the memory 225. The interconnect220 is used to transmit programming instructions and application databetween each CPU 205, I/O device interface 210, storage 230, networkinterface 215, and memory 225. The interconnect 220 may be one or morebusses. The CPUs 205 may be a single CPU, multiple CPUs, or a single CPUhaving multiple processing cores in various embodiments. In oneembodiment, a processor 205 may be a digital signal processor (DSP). Oneor more processing elements 235 (described below) may be stored in thememory 225. A processing element 235 may include one or more streamoperators 240 (described below). In one embodiment, a processing element235 is assigned to be executed by only one CPU 205, although in otherembodiments the stream operators 240 of a processing element 235 mayinclude one or more threads that are executed on two or more CPUs 205.The memory 225 is generally included to be representative of a randomaccess memory, e.g., Static Random Access Memory (SRAM), Dynamic RandomAccess Memory (DRAM), or Flash. The storage 230 is generally included tobe representative of a non-volatile memory, such as a hard disk drive,solid state device (SSD), or removable memory cards, optical storage,flash memory devices, network attached storage (NAS), or connections tostorage area network (SAN) devices, or other devices that may storenon-volatile data. The network interface 215 is configured to transmitdata via the communications network 120.

A streams application may include one or more stream operators 240 thatmay be compiled into a “processing element” container 235. The memory225 may include two or more processing elements 235, each processingelement having one or more stream operators 240. Each stream operator240 may include a portion of code that processes tuples flowing into aprocessing element and outputs tuples to other stream operators 240 inthe same processing element, in other processing elements, or in boththe same and other processing elements in a stream computingapplication. Processing elements 235 may pass tuples to other processingelements that are on the same compute node 110 or on other compute nodesthat are accessible via communications network 120. For example, aprocessing element 235 on compute node 110A may output tuples to aprocessing element 235 on compute node 110B.

The storage 230 may include a buffer 260. Although shown as being instorage, the buffer 260 may be located in the memory 225 of the computenode 110 or in a combination of both memories. Moreover, storage 230 mayinclude storage space that is external to the compute node 110, such asin a cloud.

FIG. 3 is a more detailed view of the management system 105 of FIG. 1according to some embodiments. The management system 105 may include,without limitation, one or more processors (CPUs) 305, a networkinterface 315, an interconnect 320, a memory 325, and a storage 330. Themanagement system 105 may also include an I/O device interface 310connecting I/O devices 312, e.g., keyboard, display, and mouse devices,to the management system 105.

Each CPU 305 retrieves and executes programming instructions stored inthe memory 325 or storage 330. Similarly, each CPU 305 stores andretrieves application data residing in the memory 325 or storage 330.The interconnect 320 is used to move data, such as programminginstructions and application data, between the CPU 305, I/O deviceinterface 310, storage unit 330, network interface 315, and memory 325.The interconnect 320 may be one or more busses. The CPUs 305 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 305 may bea DSP. Memory 325 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 330 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or the cloud. Thenetwork interface 315 is configured to transmit data via thecommunications network 120.

The memory 325 may store a stream manager 134. Additionally, the storage330 may store an operator graph 335. The operator graph 335 may definehow tuples are routed to processing elements 235 (FIG. 2) forprocessing.

FIG. 4 is a more detailed view of the compiler system 102 of FIG. 1according to some embodiments. The compiler system 102 may include,without limitation, one or more processors (CPUs) 405, a networkinterface 415, an interconnect 420, a memory 425, and storage 430. Thecompiler system 102 may also include an I/O device interface 410connecting I/O devices 412, e.g., keyboard, display, and mouse devices,to the compiler system 102.

Each CPU 405 retrieves and executes programming instructions stored inthe memory 425 or storage 430. Similarly, each CPU 405 stores andretrieves application data residing in the memory 425 or storage 430.The interconnect 420 is used to move data, such as programminginstructions and application data, between the CPU 405, I/O deviceinterface 410, storage unit 430, network interface 415, and memory 425.The interconnect 420 may be one or more busses. The CPUs 405 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 405 may bea DSP. Memory 425 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 430 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, Flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or to the cloud. Thenetwork interface 415 is configured to transmit data via thecommunications network 120.

The memory 425 may store a compiler 136. The compiler 136 compilesmodules, which include source code or statements, into the object code,which includes machine instructions that execute on a processor. In oneembodiment, the compiler 136 may translate the modules into anintermediate form before translating the intermediate form into objectcode. The compiler 136 may output a set of deployable artifacts that mayinclude a set of processing elements and an application descriptionlanguage file (ADL file), which is a configuration file that describesthe streaming application. In some embodiments, the compiler 136 may bea just-in-time compiler that executes as part of an interpreter. Inother embodiments, the compiler 136 may be an optimizing compiler. Invarious embodiments, the compiler 136 may perform peepholeoptimizations, local optimizations, loop optimizations, inter-proceduralor whole-program optimizations, machine code optimizations, or any otheroptimizations that reduce the amount of time required to execute theobject code, to reduce the amount of memory required to execute theobject code, or both.

The compiler 136 may also provide the application administrator with theability to optimize performance through profile-driven fusionoptimization. Fusing operators may improve performance by reducing thenumber of calls to a transport. While fusing stream operators mayprovide faster communication between operators than is available usinginter-process communication techniques, any decision to fuse operatorsrequires balancing the benefits of distributing processing acrossmultiple compute nodes with the benefit of faster inter-operatorcommunications. The compiler 136 may automate the fusion process todetermine how to best fuse the operators to be hosted by one or moreprocessing elements, while respecting user-specified constraints. Thismay be a two-step process, including compiling the application in aprofiling mode and running the application, then re-compiling and usingthe optimizer during this subsequent compilation. The end result may,however, be a compiler-supplied deployable application with an optimizedapplication configuration.

FIG. 5 illustrates an exemplary operator graph 500 for a streamcomputing application beginning from one or more sources 135 through toone or more sinks 504, 506, according to some embodiments. This flowfrom source to sink may also be generally referred to herein as anexecution path. Although FIG. 5 is abstracted to show connectedprocessing elements PE1-PE10, the operator graph 500 may include dataflows between stream operators 240 (FIG. 2) within the same or differentprocessing elements. Typically, processing elements, such as processingelement 235 (FIG. 2), receive tuples from the stream as well as outputtuples into the stream (except for a sink—where the stream terminates,or a source—where the stream begins).

The example operator graph shown in FIG. 5 includes ten processingelements (labeled as PE1-PE10) running on the compute nodes 110A-110D. Aprocessing element may include one or more stream operators fusedtogether to form an independently running process with its own processID (PID) and memory space. In cases where two (or more) processingelements are running independently, inter-process communication mayoccur using a “transport,” e.g., a network socket, a TCP/IP socket, orshared memory. However, when stream operators are fused together, thefused stream operators can use more rapid communication techniques forpassing tuples among stream operators in each processing element.

The operator graph 500 begins at a source 135 and ends at a sink 504,506. Compute node 110A includes the processing elements PE1, PE2, andPE3. Source 135 flows into the processing element PE1, which in turnoutputs tuples that are received by PE2 and PE3. For example, PE1 maysplit data attributes received in a tuple and pass some data attributesin a new tuple to PE2, while passing other data attributes in anothernew tuple to PE3. As a second example, PE1 may pass some received tuplesto PE2 while passing other tuples to PE3. Tuples that flow to PE2 areprocessed by the stream operators contained in PE2, and the resultingtuples are then output to PE4 on compute node 110B. Likewise, the tuplesoutput by PE4 flow to operator sink PE6 504. Similarly, tuples flowingfrom PE3 to PE5 also reach the operators in sink PE6 504. Thus, inaddition to being a sink for this example operator graph, PE6 could beconfigured to perform a join operation, combining tuples received fromPE4 and PE5. This example operator graph also shows tuples flowing fromPE3 to PE7 on compute node 110C, which itself shows tuples flowing toPE8 and looping back to PE7. Tuples output from PE8 flow to PE9 oncompute node 110D, which in turn outputs tuples to be processed byoperators in a sink processing element, for example PE10 506.

The tuple received by a particular processing element 235 (FIG. 2) isgenerally not considered to be the same tuple that is output downstream.Typically, the output tuple is changed in some way. An attribute ormetadata may be added, deleted, or changed. However, it is not requiredthat the output tuple be changed in some way. Generally, a particulartuple output by a processing element may not be considered to be thesame tuple as a corresponding input tuple even if the input tuple is notchanged by the processing element. However, to simplify the presentdescription and the claims, an output tuple that has the same dataattributes as a corresponding input tuple may be referred to herein asthe same tuple.

Processing elements 235 (FIG. 2) may be configured to receive or outputtuples in various formats, e.g., the processing elements or streamoperators could exchange data marked up as XML documents. Furthermore,each stream operator 240 within a processing element 235 may beconfigured to carry out any form of data processing functions onreceived tuples, including, for example, writing to database tables orperforming other database operations such as data joins, splits, reads,etc., as well as performing other data analytic functions or operations.

The stream manager 134 of FIG. 1 may be configured to monitor a streamcomputing application running on compute nodes, e.g., compute nodes110A-110D, as well as to change the deployment of an operator graph,e.g., operator graph 132. The stream manager 134 may move processingelements from one compute node 110 to another, for example, to managethe processing loads of the compute nodes 110A-110D in the computinginfrastructure 100. Further, stream manager 134 may control the streamcomputing application by inserting, removing, fusing, un-fusing, orotherwise modifying the processing elements and stream operators (orwhat tuples flow to the processing elements) running on the computenodes 110A-110D. One example of a stream computing application is IBM®'sInfoSphere® Streams (note that InfoSphere® is a trademark ofInternational Business Machines Corporation, registered in manyjurisdictions worldwide).

Because a processing element may be a collection of fused streamoperators, it is equally correct to describe the operator graph as oneor more execution paths between specific stream operators, which mayinclude execution paths to different stream operators within the sameprocessing element. FIG. 5 illustrates execution paths betweenprocessing elements for the sake of clarity.

FIG. 6 is a flowchart illustrating a method 600 to determine one or moredata processing parameters in a streaming application, according to someembodiments. Generally, the operations of method 600 may establish a setof data processing parameters. In some embodiments, data processingparameters may be time-based. In other embodiments, data processingparameters may be based on changes to the data or events caused by thedata. Other types of similar data processing parameters are alsocontemplated. Data processing parameters, according to some embodiments,will be described in further detail in accordance with FIG. 7 below.

As shown in FIG. 6, method 600 may begin at operation 605, when a streamoperator receives an input tuple. In some embodiments, the streamoperator may add an input timestamp to the tuple. The input timestampmay be added as an additional attribute of the tuple in someembodiments. In other embodiments, the input timestamp may be added tometadata describing the tuple. In yet other embodiments, the timestampmay be added as an attribute to a new tuple that is associated with theinput tuple and may flow through the application along with the inputtuple. The stream operator may, at operation 610, complete some type ofprocessing on the tuple. For example, the stream operator may searchtext and pull out certain information from the text that may beimportant in the particular streaming application.

At operation 615, the stream operator may add or modify one or more dataprocessing parameters describing the processing of the tuple. In someembodiments, the stream operator may add a new data processingparameter. The stream operator may add a new data processing parameterif there are no data processing parameters describing the tuple. Thestream operator may add a new data processing parameter if one or moredata processing parameters already describe the tuple as long as the newdata processing parameter does not already exist. The stream operatormay also add a new data processing parameter even when the dataprocessing parameter already exists. This may be the case when the dataprocessing parameter already exists, but the current value is differentor multiple values are needed. For example, a stream operator maydetermine that a timestamp for the receipt of the tuple already exists,but another timestamp may be added in order to determine the processingtime for the tuple at that stream operator.

In some embodiments, the stream operator may modify one or more dataprocessing parameters. For example, a data processing parameter maydescribe the punctuation count of the tuple. The punctuation count maybe different at the current stream operator than at the stream operatorthat last processed the tuple. In such a case, the stream operator maymodify the punctuation count so that it contains a current value. Atoperation 620, the tuple may be output to another stream operatoraccording to the operator graph of the streaming application. Though theshown embodiment illustrates operation 615 occurring prior to operation620, in some embodiments, the tuple may be output to another streamoperator, which may then execute operation 615.

FIG. 7 is a diagram illustrating examples of data processing parameters700 corresponding to operation 615 (FIG. 6) to add or modify one or moredata processing parameters 700 to a tuple, according to someembodiments. A data processing parameter 700 may include: a dataprocessing parameter 700 based on a data modification parameter 702,such as data modification parameters 728-732; a data processingparameter 700 based on a data event parameter 704, such as data eventparameters 718-726; or (3) a data processing parameter 700 that is atime parameter 706, such as time parameters 708-716. While these dataprocessing parameters 700 are exemplary according to the embodimentsdisclosed herein, other types of data processing parameters 700 may bepossible. Some embodiments may include no data processing parameters700. Other embodiments may include one or more data processingparameters 700 that are defined by one type of data processing parameter700 or a combination of a plurality of types 702-706 of data processingparameters 700.

One or more data processing parameters 700 may be defined using a datamodification parameter 702, according to some embodiments. A datamodification parameter 702, as referred to herein, may include changesmade to a tuple. Exemplary data modification parameters 702 aredisclosed herein, though other similar types of data modificationparameters are contemplated. For example, when a tuple is processed at astream operator, one or more attributes of the tuple may be modified.Modification of the tuple may include any type of alteration, such asadding one or more attributes, removing one or more attributes, orchanging one or more attributes. Changing one or more attributes mayinclude altering the value of the attribute, the type of attribute, orthe characteristics of an attribute, e.g., length of a string. The datamodification parameter 702 may be based on the type of change. In someembodiments, the data modification parameter may be defined by thenumber of attributes added 728 or the number of attributes removed 730from the tuple. In other embodiments, the data modification parameter702 may be defined by how much the processing has changed one or moreattributes 732. In yet other embodiments, there may be a plurality ofdata processing parameters 702 that are defined based on one or moretypes of changes 728-732.

One or more data processing parameters 700 may be defined based on dataevent parameters 704, according to some embodiments. A data eventparameter 704, as referred to herein, may include an action caused byprocessing a tuple. Exemplary data event parameters 704 are disclosedherein, though other similar types of data event parameters arecontemplated. An action caused by processing a tuple may include thenumber of times a tuple goes through a loop 718, the number ofexceptions handled 720 as a result of processing the tuple, the numberof tuples generated from the tuple 722, the number of windowingconditions triggered 724 by the tuple, or the punctuation count 726. Insome embodiments, a data event parameter 700 may include one or more ofany of the actions 718-726. In other embodiments, a data event parametermay include one or more of one type of action 718-726.

One or more data processing parameters 700 may be defined based on timeparameters 706, according to some embodiments. A time parameter 706, asreferred to herein, may include a time-based statistic that may describethe time it took to complete some processing in the current application.Exemplary time parameters 706 are disclosed herein, though other similartypes of time parameters are contemplated. A time parameter 706 mayinclude the processing time at the current stream operator 708. In someembodiments, a time parameter 706 may include the processing time at aprevious group of stream operators 710. The previous group of streamoperator may be either downstream or upstream of the current streamoperator. For example, the previous group of stream operators mayinclude a plurality of stream operators that processed the tuple priorto the current stream operator. In other embodiments, a time parameter706 may include the processing time at one or more previous levels 712of stream operators. A time parameter 706 may include the entire timethe tuple has spent in the streaming application 714. In yet otherembodiments, a time parameter 706 may also be defined by thetransmission time between one or more stream operators 716. The timeparameter 706 may include any or all of the parameters defined in708-716, according to some embodiments. In some embodiments, the timeparameter 706 may include only timestamps, which may allow anotherstream operator to compute the processing times described above.

FIG. 8 is a flowchart illustrating a method 800 to route tuples in astreaming application, according to some embodiments. Generally, theoperations of method 800 may modify the processing within an operatorgraph by routing tuples to an execution path that contains a modifiedversion of processing for the data stream. A stream operator may, forexample, route a tuple to a secondary execution path when one or moredata processing parameters falls outside a threshold. The threshold maybe a system-defined feature in some embodiments, while in otherembodiments the threshold may be defined by an application programmer.In yet other embodiments, the threshold may be system-defined, butcapable of being overridden by an application programmer.

As shown in FIG. 8, method 800 may begin at operation 805, when a streamoperator receives an input tuple. At operation 810, the stream operatormay read the one or more data processing parameters. If there are nodata processing parameters present, the stream operator may executemethod 600 defined above. The values of the data processing parametersmay be additional attributes of the tuple in some embodiments. In otherembodiments, the values of the data processing parameters may be part ofthe tuple metadata. In yet other embodiments, the values of the dataprocessing parameters may be part of one or more tuples that wasgenerated to contain the values of one or more data processingparameters for a particular tuple. At operation 812, the stream operatormay compare the value of one or more of the data processing parameterswith a corresponding threshold value.

At operation 815, the stream operator may determine whether the valuesof the data processing parameters indicate that processing of the tupleshould be modified. The determination may result in a modification ifthe value of one or more of the data processing parameters falls outsidea threshold for the corresponding data processing parameter. Thethreshold value for the various data processing parameters may beprovided as a default value by the streaming application system. In someembodiments, the system-defined default threshold may be configurable bythe application programmer. In other embodiments, there may be nosystem-defined threshold value, but an application programmer may beable to supply the threshold value. In yet other embodiments, thethreshold value may be determined based on application metrics about thestreaming application. If the value of the data processing parameter asdetermined at operation 812 falls outside the threshold, then processingof the tuple may be modified at operation 820. Modifying processing mayinclude routing the tuple down an execution path that contains one ormore stream operators that are configured differently than a primaryexecution path. For example, the modified processing may include eithermore or fewer stream operators than the primary execution path in someembodiments. In other embodiments, the modified processing may includemore or less extensive processing than the primary execution path. Inyet other embodiments, modified processing may be a different type ofprocessing. If the value of the data processing parameter does not falloutside the threshold value, then the tuple may be routed to the primaryexecution path at operation 825. Routing, as referred to herein, mayrefer to a stream operator outputting a tuple such that it is receivedas an input to another stream operator.

FIG. 9 illustrates a more detailed view of an operator graph 900 of astreaming application in which processing of tuples may be modifiedbased on one or more data processing parameters, according to someembodiments. Operator graph 900 will be discussed by way of an exampleembodiment—a sample streaming application in which a news company isexamining a news feed.

Operator graph 900 may include a source 135, one or more streamoperators 902-938, and one or more sinks 940-946. In some embodiments,stream operator 902 may receive a data stream from source 135. The datastream may include a news feed. The individual tuples of the data streammay include the text of a news article. Depending on the source of thenews, stream operator 902 may output a particular tuple to executionpath 954, which includes stream operators 904-914. The execution path954 may be configured to complete some type of processing on aparticular tuple. For example, the execution path 954 may compile keyterms and data about the article.

Execution path 952 may include stream operators 916-938 and may alsoinclude execution paths 948 and 950. Execution path 952 may processtuples that include news articles for different sources than thoseprocessed by execution path 954. Stream operator 918 of execution path952 may be configured in accordance with this disclosure to determinehow to route the tuples it receives to either execution path 948 or 950.Stream operator 918 may examine one or more data processing parameters,e.g., data processing parameters 700, to make this determination. Forexample, stream operator 916 may search the text of the article andcompile the location names, people names, and organization names. Streamoperator 918 may then look at the one or ore data processing parametersfor the tuple it receives. The tuple may, for example, have dataprocessing parameters related to the processing time at stream operator916. In this particular streaming application, there may be a thresholdfor processing time which could serve as an indication that streamoperator 916 may be compiling a large amount of information if theprocessing time falls outside that threshold. If the processing timedoes fall outside the threshold, stream operator 918 may route theparticular tuple to execution path 948 for some type of differentprocessing than execution path 950 (which may be for cases with lessinformation). The tuple may then proceed through execution path 950. Ifthe processing time falls within the threshold, then stream operator 918may be routed to execution path 950.

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Furthermore, although embodiments of this disclosure mayachieve advantages over other possible solutions or over the prior art,whether or not a particular advantage is achieved by a given embodimentis not limiting of this disclosure. Thus, the described aspects,features, embodiments, and advantages are merely illustrative and arenot considered elements or limitations of the appended claims exceptwhere explicitly recited in a claim(s).

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.), or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination thereof. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination thereof. In the context ofthis disclosure, a computer readable storage medium may be any tangiblemedium that can contain, or store, a program for use by or in connectionwith an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wire line, optical fiber cable, RF, etc., or any suitable combinationthereof.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including: (a) an object oriented programminglanguage such as Java, Smalltalk, C++, or the like; (b) conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages; and (c) a streams programminglanguage, such as IBM Streams Processing Language (SPL). The programcode may execute as specifically described herein. In addition, theprogram code may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer, or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

Aspects of the present disclosure have been described with reference toflowchart illustrations, block diagrams, or both, of methods,apparatuses (systems), and computer program products according toembodiments of this disclosure. It will be understood that each block ofthe flowchart illustrations or block diagrams, and combinations ofblocks in the flowchart illustrations or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing the functionsor acts specified in the flowchart or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function or act specified in the flowchart or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions or acts specified in the flowchart or blockdiagram block or blocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams or flowchart illustration, andcombinations of blocks in the block diagrams or flowchart illustration,can be implemented by special purpose hardware-based systems thatperform the specified functions or acts, or combinations of specialpurpose hardware and computer instructions.

Although embodiments are described within the context of a streamcomputing application, this is not the only context relevant to thepresent disclosure. Instead, such a description is without limitationand is for illustrative purposes only. Of course, one of ordinary skillin the art will recognize that embodiments of the present invention maybe configured to operate with any computer system or application capableof performing the functions described herein. For example, embodimentsof the invention may be configured to operate in a clustered environmentwith a standard database processing application.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the invention may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow.

1. A method for processing a stream of tuples, comprising: receiving astream of tuples to be processed by a plurality of processing elementsoperating on one or more computer processors, each processing elementhaving one or more stream operators; determining one or more parametersat a first stream operator, wherein the one or more parameters representa processing history of a tuple at the first stream operator;associating the one or more parameters with the tuple; determining by asecond stream operator whether to modify processing of the tuple,wherein the determining is based on the one or more parameters; andmodifying processing of the tuple if a parameter falls outside athreshold.
 2. The method of claim 1, wherein the first stream operatoris a plurality of stream operators.
 3. The method of claim 1, whereinthe one or more parameters are time-based.
 4. The method of claim 3,wherein the one or more parameters include an amount of time to processthe tuple at the first stream operator.
 5. The method of claim 3,wherein the one or more parameters include an amount of time to processthe tuple at a previous group of stream operators.
 6. The method ofclaim 3, wherein the one or more parameters include transmission timebetween a plurality of stream operators.
 7. The method of claim 3,wherein the one or more parameters include how long the tuple has beenin an application.
 8. The method of claim 1, wherein the one or moreparameters are based on modifications to the tuple.
 9. The method ofclaim 8, wherein the one or more parameters include a count of how manyattributes have been added to the tuple.
 10. The method of claim 8,wherein the one or more parameters include how many attributes have beenremoved from the tuple.
 11. The method of claim 8, wherein the one ormore parameters include changes to one or more attributes of the tuple.12. The method of claim 1, wherein the one or more parameters are basedon one or more events triggered by the tuple.
 13. The method of claim12, wherein the one or more parameters include a count of how many timesthe tuple has flowed through an execution loop.
 14. The method of claim12, wherein the one or more parameters include a count of how manyexceptions the tuple has caused an application to handle.
 15. The methodof claim 12, wherein the one or more parameters include a count of howmany tuples were generated from the tuple.
 16. The method of claim 1,wherein the modifying processing of the tuple includes routing the tupleto a modified execution path.
 17. The method of claim 1, whereinassociating the one or more parameters with the tuple includes modifyingmetadata for the tuple. 18-24. (canceled)